Network architecture for the joint learning of monocular depth prediction and completion

ABSTRACT

System, methods, and other embodiments described herein relate to determining depths of a scene from a monocular image. In one embodiment, a method includes generating depth features from sensor data according to whether the sensor data includes sparse depth data. The method includes selectively injecting the depth features into a depth model. The method includes generating a depth map from at least a monocular image using the depth model that is guided by the depth features when injected. The method includes providing the depth map as depth estimates of objects represented in the monocular image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Application No.63/112,234, filed on, Nov. 11, 2020, which are herein incorporated byreference in their entirety.

TECHNICAL FIELD

The subject matter described herein relates, in general, to systems andmethods for determining depths of a scene from a monocular image, and,more particularly, to a unique network architecture that performs depthprediction and depth completion.

BACKGROUND

Various devices that operate autonomously or that provide informationabout a surrounding environment often use sensors that facilitateperceiving obstacles and additional aspects of the surroundingenvironment. As one example, a robotic device uses information from thesensors to develop awareness of the surrounding environment in order tonavigate through the environment and avoid hazards. In particular, therobotic device uses the perceived information to determine a 3-Dstructure of the environment so that the device may distinguish betweennavigable regions and potential hazards. The ability to perceivedistances using sensor data provides the robotic device with the abilityto plan movements through the environment and generally improvesituational awareness about the environment.

In one approach, the robotic device may employ monocular cameras tocapture images of the surrounding environment. While this approach canavoid the use of expensive light detection and ranging (LiDAR) sensors,the captured images do not explicitly include depth information.Instead, the robotic device can implement processing routines thatderive depth information from the monocular images. Using monocularimages alone to derive depth information can encounter difficulties,such as depth inaccuracies and various types of aberrations. Similarly,using LiDAR data alone to provide depth information also presentsdifficulties, such as high computational loads from the amount of dataor issues with depth completion when the data is sparse. Consequently,difficulties persist with deriving depth data in a reliable manner.

SUMMARY

In one embodiment, example systems and methods relate to a novel networkarchitecture for performing depth prediction and depth completion. Aspreviously noted, different approaches to providing information aboutdepth are generally associated with different types of sensor data.Moreover, the separate approaches generally suffer from differentdifficulties. For example, in the context of using monocular images,inaccuracies in metric scale can be a difficulty, whereas in the contextof using explicit range data (e.g., LiDAR data), computationalrequirements can represent a specific difficulty.

Therefore, in one arrangement, a novel network architecture is disclosedthat leverages both monocular images and range data (i.e., sparse depthdata) to provide an improved output about the depth of aspects depictedin the monocular images/range data. For example, the novel networkarchitecture includes a depth model that can use monocular images aloneto provide depth estimates or that can also use sparse depth dataprovided via an integrated sparse auxiliary network (SAN) to deriveimproved depth estimates. Thus, the depth model can be said to beperforming depth prediction and/or depth completion depending on thedata that is available via the inputs.

As such, the depth model is more robust than a model utilizing a singleinput stream since the depth model can selectively integrate the sparsedepth data into the depth estimates as the sparse depth data isavailable. That is, for example, the sensors of the device (e.g., avehicle) may encounter difficulties, such as hardware failures duringoperation. As such, when the sparse depth data is unavailable, the depthmodel is still capable of producing depth estimates according to themonocular image. Moreover, because the depth model can operate withoutthe sparse depth data, the depth model can be leveraged in variousconfigurations that provide monocular images without explicit depthdata, thereby improving the usability of the depth model overall.

In any case, the depth model implements an additional encoder, which isreferred to herein as the sparse auxillary network (SAN), to process thesparse depth data and inject depth features derived from the sparsedepth data into an encoder/decoder structure of the depth model. In atleast one arrangement, the SAN injects the depth features via skipconnections of encoder/decoder structure. The skip connections provide,for example, residual information about the encoding of image featuresat different resolutions between the encoder and the decoder. Thus, theSAN injects the depth features via the skip connections according tocorresponding spatial dimensions of the image features, respectively.The depth model concatenates the image features and the depth featuresand provides the concatenated features into the decoder of theencoder/decoder structure, which then produces the depth estimates. Inthis way, the disclosed novel network architecture functions to improvethe depth estimates through the use of monocular images in combinationwith sparse depth data while providing a robust framework.

In one embodiment, a depth system is disclosed. The depth systemincludes one or more processors and a memory communicably coupled to theone or more processors. The memory stores a network module includinginstructions that, when executed by the one or more processors, causethe one or more processors to generate depth features from sensor dataaccording to whether the sensor data includes sparse depth data. Thenetwork module includes instructions to selectively inject the depthfeatures into a depth model. The network module includes instructions togenerate a depth map from at least a monocular image using the depthmodel that is guided by the depth features when injected. The networkmodule includes instructions to provide the depth map as depth estimatesof objects represented in the monocular image.

In one embodiment, a non-transitory computer-readable medium includinginstructions that when executed by one or more processors cause the oneor more processors to perform various functions is disclosed. Theinstructions include instructions to generate depth features from sensordata according to whether the sensor data includes sparse depth data.The instructions include instructions to selectively inject the depthfeatures into a depth model. The instructions include instructions togenerate a depth map from at least a monocular image using the depthmodel that is guided by the depth features when injected. Theinstructions include instructions to provide the depth map as depthestimates of objects represented in the monocular image.

In one embodiment, a method is disclosed. The method includes generatingdepth features from sensor data according to whether the sensor dataincludes sparse depth data. The method includes selectively injectingthe depth features into a depth model. The method includes generating adepth map from at least a monocular image using the depth model that isguided by the depth features when injected. The method includesproviding the depth map as depth estimates of objects represented in themonocular image.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various systems, methods, andother embodiments of the disclosure. It will be appreciated that theillustrated element boundaries (e.g., boxes, groups of boxes, or othershapes) in the figures represent one embodiment of the boundaries. Insome embodiments, one element may be designed as multiple elements ormultiple elements may be designed as one element. In some embodiments,an element shown as an internal component of another element may beimplemented as an external component and vice versa. Furthermore,elements may not be drawn to scale.

FIG. 1 illustrates one embodiment of a vehicle within which systems andmethods disclosed herein may be implemented.

FIG. 2 illustrates one embodiment of a depth system that is associatedwith providing depth estimates according to monocular images and sparsedepth data.

FIGS. 3A-C illustrate different examples of depth data.

FIG. 4 illustrates a diagram of one embodiment of an architecture of adepth model.

FIG. 5 illustrates a detailed diagram of one embodiment of a depthmodel.

FIG. 6 illustrates a flowchart of one embodiment of a method associatedwith generating depth maps using a depth model that can use monocularimages and sparse depth data.

DETAILED DESCRIPTION

Systems, methods, and other embodiments associated with a novel networkarchitecture for performing depth prediction and depth completion aredisclosed. As previously noted, different approaches to providinginformation about depth are generally associated with different types ofsensor data. Moreover, the separate approaches generally suffer fromdifferent difficulties. For example, in the context of using monocularimages, inaccuracies in metric scale can be a difficulty that arisesfrom using monocular images without a source of ground truth depth.Additionally, in the context of using explicit range data (e.g., LiDARdata), computational requirements from the quantity of data and/orextrapolating sparse data into a complete representation can represent aspecific difficulty with acquiring comprehensive depth information.

Therefore, in one arrangement, a novel network architecture is disclosedthat leverages both monocular images and range data (i.e., sparse depthdata) to provide an improved depth map as an output. For example, thenovel network architecture includes a depth model that can useinformation in addition to monocular images to provide depth estimates.The depth model uses monocular images and can also selectively integratesparse depth data into the process of generating the depth map whenavailable. To achieve this, in one arrangement, the depth model includesan additional structure in the form of an integrated sparse auxiliarynetwork (SAN) that functions alongside an encoder/decoder structure ofthe depth model.

As such, the depth model is more robust than a model utilizing a singleinput stream since the depth model can selectively integrate the sparsedepth data into the depth estimates as the sparse depth data isavailable. For example, the sensors of the device (e.g., a vehicle) mayencounter difficulties, such as hardware failures, software failures, orother circumstances during operation that make the explicit depth dataunavailable. As such, when the sparse depth data is unavailable, thedepth model is still capable of producing depth estimates according tothe monocular image.

In any case, the depth model implements an additional encoder, which isreferred to herein as the sparse auxillary network (SAN), to process thesparse depth data. The SAN is a machine learning algorithm, such as aconvolutional neural network (CNN). The SAN accepts range information inthe form of the sparse depth data from a range sensor and outputs depthfeatures. The sparse depth data is range/distance information providedby a range sensor, such as a LiDAR or other range sensor. The depth datais referred to as being sparse since the depth data is not provided on aper-pixel basis as in the case of a depth map corresponding to amonocular image but instead may sparsely depict distances across anobserved scene.

The depth model, in at least one arrangement, injects depth featuresderived from the sparse depth data into an encoder/decoder structure ofthe depth model. In at least one configuration, the SAN injects thedepth features via skip connections of encoder/decoder structure. Theskip connections provide, for example, residual information about theencoding of image features at different resolutions between the encoderand the decoder. Thus, the SAN injects the depth features via the skipconnections according to corresponding spatial dimensions of the imagefeatures. The depth model concatenates the image features and the depthfeatures and provides the concatenated features into the decoder of theencoder/decoder structure to provide the depth features as a passiveinput into the decoder. In this way, the disclosed novel networkarchitecture functions to improve the depth estimates through the use ofmonocular images in combination with sparse depth data while providing arobust framework that continues to function in the absence of the sparsedepth data.

Referring to FIG. 1 , an example of a vehicle 100 is illustrated. Asused herein, a “vehicle” is any form of powered transport. In one ormore implementations, the vehicle 100 is an automobile. Whilearrangements will be described herein with respect to automobiles, itwill be understood that embodiments are not limited to automobiles. Insome implementations, the vehicle 100 may be any robotic device or formof powered transport that, for example, observes surroundings to providedeterminations therefrom, and thus benefits from the functionalitydiscussed herein. In yet further embodiments, the vehicle 100 may be astatically mounted device, an embedded device, or another device thatuses monocular images to derive depth information about a scene insteadof being a motive device.

In any case, the vehicle 100 also includes various elements. It will beunderstood that, in various embodiments, it may not be necessary for thevehicle 100 to have all of the elements shown in FIG. 1 . The vehicle100 can have any combination of the various elements shown in FIG. 1 .Further, the vehicle 100 can have additional elements to those shown inFIG. 1 . In some arrangements, the vehicle 100 may be implementedwithout one or more of the elements shown in FIG. 1 . While the variouselements are illustrated as being located within the vehicle 100, itwill be understood that one or more of these elements can be locatedexternal to the vehicle 100. Further, the elements shown may bephysically separated by large distances and provided as remote services(e.g., cloud-computing services, software-as-a-service (SaaS), etc.).

Some of the possible elements of the vehicle 100 are shown in FIG. 1 andwill be described along with subsequent figures. However, a descriptionof many of the elements in FIG. 1 will be provided after the discussionof FIGS. 2-6 for purposes of the brevity of this description.Additionally, it will be appreciated that for simplicity and clarity ofillustration, where appropriate, reference numerals have been repeatedamong the different figures to indicate corresponding or analogouselements. In addition, the discussion outlines numerous specific detailsto provide a thorough understanding of the embodiments described herein.Those of skill in the art, however, will understand that the embodimentsdescribed herein may be practiced using various combinations of theseelements.

In any case, the vehicle 100 includes a depth system 170 that functionsto generate depth estimates (i.e., depth maps) using a novel networkarchitecture that can employ multiple sources of information. Moreover,while depicted as a standalone component, in one or more embodiments,the depth system 170 is integrated with the autonomous driving module160, the camera 126, or another component of the vehicle 100.Additionally, as noted previously, one or more components of the depthsystem 170 may be cloud-based elements that are remote from the vehicle100. The noted functions and methods will become more apparent with afurther discussion of the figures.

With reference to FIG. 2 , one embodiment of the depth system 170 isfurther illustrated. The depth system 170 is shown as including aprocessor 110. Accordingly, the processor 110 may be a part of the depthsystem 170, or the depth system 170 may access the processor 110 througha data bus or another communication path. In one or more embodiments,the processor 110 is an application-specific integrated circuit (ASIC)that is configured to implement functions associated with a networkmodule 220. In general, the processor 110 is an electronic processor,such as a microprocessor that is capable of performing various functionsas described herein. In one embodiment, the depth system 170 includes amemory 210 that stores the network module 220 and/or other modules thatmay function in support of generating depth information. The memory 210is a random-access memory (RAM), read-only memory (ROM), a hard diskdrive, a flash memory, or other suitable memory for storing the module220. The network module 220 is, for example, computer-readableinstructions that, when executed by the processor 110, cause theprocessor 110 to perform the various functions disclosed herein. Infurther arrangements, the network module 220 is a combination of logicgates, an integrated circuit, or a purpose-built processor.

Furthermore, in one embodiment, the depth system 170 includes a datastore 230. The data store 230 is, in one embodiment, an electronic datastructure stored in the memory 210 or another data store, and that isconfigured with routines that can be executed by the processor 110 foranalyzing stored data, providing stored data, organizing stored data,and so on. Thus, in one embodiment, the data store 230 stores data usedby the module 220 in executing various functions. For example, asdepicted in FIG. 2 , the data store 230 includes sensor data 240, adepth model 250, and a depth map 260 along with, for example, otherinformation that is used and/or produced by the module 220.

The sensor data 240 includes, for example, monocular images from thecamera 126 or another imaging device. The monocular images are generallyderived from one or more monocular videos that are comprised of aplurality of frames. As described herein, the monocular images are, forexample, images from the camera 126 or another imaging device that ispart of a video, and that encompasses a field-of-view (FOV) about thevehicle 100 of at least a portion of the surrounding environment. Thatis, the monocular image is, in one approach, generally limited to asubregion of the surrounding environment. As such, the image may be of aforward-facing (i.e., the direction of travel) 60, 90, 120-degree FOV, arear/side facing FOV, or some other subregion as defined by the imagingcharacteristics (e.g., lens distortion, FOV, etc.) of the camera 126. Invarious aspects, the camera 126 is a pinhole camera, a fisheye camera, acatadioptric camera, or another form of camera that acquires imageswithout a specific depth modality.

An individual monocular image itself includes visual data of the FOVthat is encoded according to an imaging standard (e.g., codec)associated with the camera 126 or another imaging device that is thesource. In general, characteristics of a source camera (e.g., camera126) and the video standard define a format of the monocular image.Thus, while the particular characteristics can vary according todifferent implementations, in general, the image has a definedresolution (i.e., height and width in pixels) and format. Thus, forexample, the monocular image is generally an RGB visible light image. Infurther aspects, the monocular image can be an infrared image associatedwith a corresponding infrared camera, a black/white image, or anothersuitable format as may be desired. Whichever format that the depthsystem 170 implements, the image is a monocular image in that there isno explicit additional modality indicating depth nor any explicitcorresponding image from another camera from which the depth can bederived (i.e., no stereo camera pair). In contrast to a stereo imagethat may integrate left and right images from separate cameras mountedside-by-side to provide an additional depth channel, the monocular imagedoes not include explicit depth information, such as disparity mapsderived from comparing the stereo images pixel-by-pixel. Instead, themonocular image implicitly provides depth information in therelationships of perspective and size of elements depicted therein fromwhich the depth model 250 derives the depth map/estimates.

Additionally, the sensor data 240, in one or more arrangements, furtherincludes depth data about a scene depicted by the associated monocularimages. The depth data indicates distances from a range sensor thatacquired the depth data to features in the surrounding environment. Thedepth data, in one or more approaches, is sparse or generally incompletefor a corresponding scene such that the depth data includes sparselydistributed points within a scene that are annotated by the depth dataas opposed to a depth map (e.g., depth map 260) that generally providescomprehensive depths for each separate depicted pixel. Consider FIGS.3A, 3B, and 3C, which depict separate examples of depth data for acommon scene. FIG. 3A depicts a depth map 300 that includes a pluralityof annotated points generally corresponding to an associated monocularimage on a per-pixel basis. Thus, the depth map 300 includes about18,288 separate annotated points.

By comparison, FIG. 3B is an exemplary 3D point cloud 310 that may begenerated by a LiDAR device having 64 scanning beams. Thus, the pointcloud 310 includes about 1,427 separate points. Even though the pointcloud 310 includes substantially fewer points than the depth map 300,the depth data of FIG. 3B represents a significant cost to acquire overa monocular video on an image-by-image basis. These costs and otherdifficulties generally relate to an expense of a robust LiDAR sensorthat includes 64 separate beams, difficulties in calibrating this typeof LiDAR device with the monocular camera, storing large quantities ofdata associated with the point cloud 310 for each separate image, and soon. As an example of sparse depth data, FIG. 3C depicts a point cloud320. In the example of point cloud 320, a LiDAR having 4 beams generatesabout 77 points that form the point cloud 320. Thus, in comparison tothe point cloud 310, the point cloud 320 includes about 5% of the depthdata as the point cloud 310, which is a substantial reduction in data.However, the sparse information depicted by point cloud 320 is generallyinsufficient to develop a comprehensive assessment of the surroundingenvironment.

As an additional comparison of the FIGS. 3A-3C, note that within FIGS.3A and 3B, the depth data is sufficiently dense to convey details ofexisting features/objects such as vehicles, etc. However, within thepoint cloud 320 of FIG. 3C, the depth data is sparse or, statedotherwise, the depth data vaguely characterizes the corresponding sceneaccording to distributed points across the scene that do not generallyprovide detail of specific features/objects depicted therein. Thus, thissparse depth data that is dispersed in a minimal manner across the scenemay not provide enough data for some purposes.

While the depth data is generally described as originating from a LiDAR,in further embodiments, the depth data may originate from a stereocamera, radar, or another range sensor. Furthermore, the depth dataitself generally includes depth/distance information relative to a pointof origin, such as the range sensor that may be further calibrated inrelation to the camera 126, and may also include coordinates (e.g., x, ywithin an image) corresponding with separate depth measurements.

Further detail about the depth system 170 of FIG. 2 , including thedepth model 250, will be provided in relation to FIG. 4 and subsequentfigures. Thus, with reference to FIG. 4 , one embodiment of the depthmodel 250 is shown. As illustrated in FIG. 4 , the depth model 250includes a sparse area network (SAN) 400, an image encoder 410, a depthdecoder 420, and skip connections 430. It should be appreciated thatwhile FIG. 4 illustrates the various aspects of the depth model 250 asbeing a separate component, in various aspects, the network module 220includes instructions to apply the depth model 250, and the depth model250 may be integrated with the network module 220.

In general, the network module 220 controls the depth model 250 toprocess the sensor data 250, which includes sparse depth data 440 and amonocular image 450, as shown in FIG. 4 . However, it should beappreciated that the sparse depth data 440 may become unavailable due tovarious circumstances, such as a sensor failure. Accordingly, the depthmodel 250, in at least one arrangement, still functions but without thedepth features from the SAN 400. That is, the image encoder 410processes the monocular image 450, but depth features are notconcatenated via the skip connections 430 into the depth decoder 420 togenerate the depth map 260. In this way, the depth model 250 is flexibleto continue operation when the sparse depth data 440 is unavailable.

In one configuration, the SAN 400 is a convolutional neural network(CNN). In further arrangements, the SAN 400 is configured with sparseconvolutions, such as Minkowski convolutions. The SAN 400 may furtherinclude sparsification and densification layers, as will be discussed ingreater detail subsequently. In any case, the SAN 400 produces depthfeatures from the sparse depth data 440. The depth features are encodedfeatures from the sparse depth data 440 that are provided at multipledifferent spatial resolutions. The different spatial resolutionscorrespond with spatial resolutions of image features from the imageencoder 410 derived from the monocular image 450. Thus, as the SAN 400processes the sparse depth data 440, the image encoder 410 processes themonocular image 450 of the same scene.

The skip connections 430 function to carry over the image features atvarying spatial dimensions into depth decoder 420 as residualinformation. As part of this, the skip connections concatenate the depthfeatures with the image features of respective corresponding spatialresolutions. Accordingly, while the depth decoder 420 receives a featuremap of image features from the image encoder 410 and iteratively decodesthe feature map through subsequent spatial dimensions, the skipconnections provide the residual image features concatenated with thedepth features into the respective iterations. In this way, the depthmodel 250 injects the sparse depth data 440 into the encoder/decoderstructure and improves an accuracy of the depth map 260 as the finaloutput.

As further detail about the depth model 250, consider FIG. 5 , whichillustrates a detailed view of the depth model 250. As shown in FIG. 5 ,the separate encoder/decoder structures 400, 410, and 420 are comprisedof multiple different layers as set forth in the included legend.Moreover, the SAN 400 further includes learned weights 500 and 510. Thelearned weights 500 adjust the influence of the depth features while thelearned weights 510 adjust the influence of the image features that areultimately concatenated with information in the image decoder 420. Bylearning the weights 500 and 510 as part of training, the depth model250 can better integrate the sparse depth data 440. Moreover, aspreviously noted, the SAN 400 can include Minkowski convolutions, adensification layer, and a sparsification layer.

As further explanation, a sparse tensor S is written as a coordinatematrix C and a feature matrix F, as shown in equation (1).

$\begin{matrix}{{C = \begin{bmatrix}u_{l} & v_{l} & s_{1} \\\vdots & \vdots & \vdots \\u_{n} & v_{n} & s_{n}\end{bmatrix}},{F = \begin{bmatrix}f_{1} \\\vdots \\f_{n}\end{bmatrix}}} & (1)\end{matrix}$

Where {u_(n), v_(n)} are pixel coordinates, s_(n) is the sample index inthe batch, and f_(n)∈

is the corresponding feature vector. Assuming a batch size of 1 anddisregard the batch index, an input W×H×1 depth map {tilde over (D)} issparsified by the sparsification layer by gathering valid pixels (i.e.,pixels with positive values) as coordinates and depth values asfeatures, such that:{tilde over (S)}={{u _(n) ,v _(n) },{d _(n) }}∀u,v∈{tilde over(D)}|{tilde over (D)}(u,v)>0.0  (2)

Similarly, a sparse tensor {tilde over (S)}={{tilde over (C)}, {tildeover (F)}} can be densified by scattering its pixel coordinates andfeature values into a dense W×H×Q matrix {tilde over (P)}, such that:

$\begin{matrix}{{\overset{˜}{P}\left( {u_{n},v_{n}} \right)} = \left\{ \begin{matrix}f_{n} & {{{if}\mspace{14mu}\left\{ {u_{n},v_{n}} \right\}} \in \overset{\sim}{C}} \\{0,} & {{otherwise}.}\end{matrix} \right.} & (3)\end{matrix}$

Once the input depth data is sparsified, the SAN 400 can encode theinformation through a series of novel Sparse Residual Blocks (SRB),which are generally comprised of sparse convolutional blocks (e.g.,Minkowski convolutions). In any case, the depth features from the SAN400 are encoded information about depths represented in the sparse depthdata while the image features are, in general, aspects of the image thatare indicative of spatial information that is intrinsically encodedtherein. One example of an architecture for the encoding layers thatform the image encoder 410 may include a series of layers that functionto fold (i.e., adapt dimensions of the feature map to retain thefeatures) encoded features into separate channels, iteratively reducingspatial dimensions of the image 450 while packing additional channelswith information about embedded states of the features. The addition ofthe extra channels avoids the lossy nature of the encoding process andfacilitates the preservation of more information (e.g., feature details)about the original monocular image 450.

Accordingly, in at least one approach, the image encoder 410 iscomprised of multiple encoding layers formed from a combination oftwo-dimensional (2D) convolutional layers, packing blocks, and residualblocks. While the image encoder 410 is presented as including the notedcomponents, it should be appreciated that further embodiments may varythe particular form of the encoding layers (e.g., convolutional andpooling layers without packing layers), and thus the noted configurationis one example of how the depth system 170 may implement the depth model250.

The separate encoding layers generate outputs in the form of encodedfeature maps (also referred to as tensors), which the encoding layersprovide to subsequent layers in the depth model 250, including specificlayers of an image decoder 420 via skip connections that may function toprovide residual information between the image encoder 410 and the imagedecoder 420. Thus, the image encoder 410 includes a variety of separatelayers that operate on the monocular image 450, and subsequently onderived/intermediate feature maps that convert the visual information ofthe monocular image 450 into embedded state information in the form ofencoded features of different channels. In any case, the output of theimage encoder 410 is, in one approach, a feature map having a particulardimension (e.g., 512×H/32×W/32) that is transformed in relation to theimage 450 (e.g., 3×H×W).

With continued reference to FIG. 5 , the depth model 250 furtherincludes the image decoder 420. One example of how the image decoder 420functions includes unfolding (i.e., adapting dimensions of the tensor toextract the features) the previously encoded spatial information inorder to derive the depth map 260 according to learned correlationsassociated with the encoded features. That is, the decoding layersgenerally function to up-sample, through sub-pixel convolutions andother mechanisms, the previously encoded features into the depth map260. In one or more arrangements, the decoding layers comprise unpackingblocks, two-dimensional convolutional layers, and inverse depth layersthat function as output layers for different spatial scales. In furtheraspects, the image decoder 420 may also receive inputs via the skipconnections 430 from another model, such as the SAN 400. While the imagedecoder 420 is presented as including the noted components, it should beappreciated that further arrangements may vary the particular form ofthe decoding layers (e.g., deconvolutional layers without unpackinglayers), and thus the noted configuration is one example of how thedepth system 170 may implement the image decoder 420.

In any case, returning to FIG. 2 , the depth system 170, in oneembodiment, employs the depth model 250 to produce the depth map 260,which, in one or more arrangements, may be provided as an inversemapping having inverse values for the depth estimates. In general,however, the depth map 260 is a pixel-wise prediction of depths for theimage 450. That is, the depth model 250 provides estimates of depths fordifferent aspects depicted in the image 450. Of course, in the presentapproach, the depth model 250 further integrates information from thesparse depth data 440 to supplement the image 450 in producing the depthmap 260.

It should be appreciated that, in one embodiment, the network module 220generally includes instructions that function to control the processor110 to execute various actions to control the depth model 250 to producethe depth map 260. The network module 220, in one or more approaches,acquires the sensor data 240 including the sparse depth data 440 and themonocular image 450 by controlling the camera 126 and a LiDAR 124 tocapture the sensor data 240 from a data bus, or electronic memory, oranother available communication pathway. Accordingly, the depth system170 may acquire the sparse depth data in parallel with the monocularimage to provide corresponding sparse depth information for the image.

Additional aspects of the joint learning of depth prediction and depthcompletion will be discussed in relation to FIG. 6 . FIG. 6 illustratesa flowchart of a method 600 that is associated with generating depthmaps using a depth model that can use both monocular images and sparsedepth data as an input. Method 600 will be discussed from theperspective of the depth system 170 of FIGS. 1-2 . While method 600 isdiscussed in combination with the depth system 170, it should beappreciated that the method 600 is not limited to being implementedwithin the depth system 170 but is instead one example of a system thatmay implement the method 600.

At 610, the network module 220 acquires the sensor data 240. In general,the sensor data 240 is comprised of at least a monocular image (e.g.,image 450), but may also include, as previously outlined, sparse depthdata. The sparse depth data can be LiDAR data or depth data from anothersource, such as a radar. In any case, the sparse depth data provides amechanism by which the depth system 170 can integrate furtherinformation about the surrounding environment in order to guide thegeneration of the depth map 260 and improve the quality of the depthestimates included therein.

At 620, the network module 220 determines whether the sensor data 240includes sparse depth data in addition to the monocular image. Forexample, as noted previously, the sensor data 240 may not include thesparse depth data for various reasons, such as an error in an associatedrange sensor, a slower refresh rate than the camera 126, processingerrors, an absence of a range sensor, and so on. Thus, the networkmodule 220 can determine when the sensor data 240 includes the sparsedepth data by, for example, analyzing the sensor data 240 for thepresence of the sparse depth data as the network module 220 receives thesensor data 240, e.g., by identifying the sparse depth data, reading adesignated header value in a packet including the sensor data 240, etc.

In any case, the network module 220 can determine the presence of thesparse depth data and may, in one or more arrangements, activate the SAN400 to generate the depth features from the sparse depth data when thesparse depth data is present. Activating the network module 220 mayinclude providing the sparse depth data to the SAN 400 for processing.

At 630, the network module 220 generates depth features from the sensordata 240 according to whether the sensor data 240 includes sparse depthdata. That is, the network module 220 selectively performs the task ofgenerating the depth features according to the presence of the sparsedepth data. Thus, when the sparse depth data is present, the networkmodule 220 uses the SAN 400 to generate the depth features from thesparse depth data. It should be appreciated that while the use of theSAN 400 is discussed sequentially prior to generating the depth map 260by the depth model 250, the SAN 400 can execute in parallel with furthercomponents of the depth model 250 (e.g., image encoder 410) in order toprovide depth features to the image decoder 420 in parallel with theimage features.

At 640, the network module 220 injects the depth features into the depthmodel 250. For example, the network module 220 injects the depthfeatures by concatenating the depth features with the image features andproviding concatenated features into the image decoder 420 of the depthmodel 250. Moreover, the network module 220 can further apply learnedweights to the depth features and the image features prior toconcatenating the separate features via skip connections of the depthmodel 250. In general, injecting the sparse depth data via the depthfeatures using the skip connections is an optimal approach tointegrating this information and provides for performing depthcompletion and prediction without degradation of either process. Thus,the learnable weights provide for further conditioning the depth andimage features, thereby enabling switching between tasks.

At 650, the network module 220 generates a depth map from at least themonocular image 450 using the depth model 250 that is selectively guidedby the depth features when injected. That is, as shown in the flowchartof FIG. 6 , the network module 220 can generate the depth map 260without using the sparse depth data by using the monocular image alone.However, when available, the addition of the sparse depth data generallyimproves the accuracy of the depth map 260. As noted in relation toFIGS. 4-5 , the network module 220 applies the depth model 250 to themonocular image by using the image encoder 410 to encode image featuresand uses the depth decoder 420 to decode the image features, and thedepth features into the depth map 260. In this way, the network module220 generates the depth map 260 as a dense representation of depths fora depicted scene of the surrounding environment.

At 660, the network module 220 provides the depth map 260 as depthestimates of objects represented in the monocular image. In onearrangement, the network module 220 provides the depth map 260 tocontrol a device (e.g., the vehicle 100) to navigate through asurrounding environment. As should be appreciated, in one arrangement,the network module 220 electronically provides the map 260 to othersystems of the vehicle 100 in support of, for example, autonomousplanning and navigation of the vehicle 100. Of course, in furtherimplementations, the network module 220 communicates the map 260 to aremote device that originally provides the sensor data 240 as a responseto an original request for depth information. In general, the depthsystem 170 and the depth model 250 can be employed in various contextsin support of active autonomous navigation, scene analysis, metadataanalysis (e.g., traffic analysis), and so on. In either case, theapproach embodied within the depth system 170 provides a unique andimproved approach to leveraging monocular images in combination withsparse depth data to resolve high-resolution dense depth data that ismetrically accurate.

FIG. 1 will now be discussed in full detail as an example environmentwithin which the system and methods disclosed herein may operate. Insome instances, the vehicle 100 is configured to switch selectivelybetween an autonomous mode, one or more semi-autonomous operationalmodes, and/or a manual mode. Such switching can be implemented in asuitable manner, now known or later developed. “Manual mode” means thatall of or a majority of the navigation and/or maneuvering of the vehicleis performed according to inputs received from a user (e.g., humandriver). In one or more arrangements, the vehicle 100 can be aconventional vehicle that is configured to operate in only a manualmode.

In one or more embodiments, the vehicle 100 is an autonomous vehicle. Asused herein, “autonomous vehicle” refers to a vehicle that operates inan autonomous mode. “Autonomous mode” refers to navigating and/ormaneuvering the vehicle 100 along a travel route using one or morecomputing systems to control the vehicle 100 with minimal or no inputfrom a human driver. In one or more embodiments, the vehicle 100 ishighly automated or completely automated. In one embodiment, the vehicle100 is configured with one or more semi-autonomous operational modes inwhich one or more computing systems perform a portion of the navigationand/or maneuvering of the vehicle along a travel route, and a vehicleoperator (i.e., driver) provides inputs to the vehicle to perform aportion of the navigation and/or maneuvering of the vehicle 100 along atravel route.

The vehicle 100 can include one or more processors 110. In one or morearrangements, the processor(s) 110 can be a main processor of thevehicle 100. For instance, the processor(s) 110 can be an electroniccontrol unit (ECU). The vehicle 100 can include one or more data stores115 for storing one or more types of data. The data store 115 caninclude volatile and/or non-volatile memory. Examples of suitable datastores 115 include RAM (Random Access Memory), flash memory, ROM (ReadOnly Memory), PROM (Programmable Read-Only Memory), EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), registers, magnetic disks, opticaldisks, hard drives, or any other suitable storage medium, or anycombination thereof. The data store 115 can be a component of theprocessor(s) 110, or the data store 115 can be operatively connected tothe processor(s) 110 for use thereby. The term “operatively connected,”as used throughout this description, can include direct or indirectconnections, including connections without direct physical contact.

In one or more arrangements, the one or more data stores 115 can includemap data 116. The map data 116 can include maps of one or moregeographic areas. In some instances, the map data 116 can includeinformation or data on roads, traffic control devices, road markings,structures, features, and/or landmarks in the one or more geographicareas. The map data 116 can be in any suitable form. In some instances,the map data 116 can include aerial views of an area. In some instances,the map data 116 can include ground views of an area, including360-degree ground views. The map data 116 can include measurements,dimensions, distances, and/or information for one or more items includedin the map data 116 and/or relative to other items included in the mapdata 116. The map data 116 can include a digital map with informationabout road geometry. The map data 116 can be high quality and/or highlydetailed.

In one or more arrangements, the map data 116 can include one or moreterrain maps 117. The terrain map(s) 117 can include information aboutthe ground, terrain, roads, surfaces, and/or other features of one ormore geographic areas. The terrain map(s) 117 can include elevation datain the one or more geographic areas. The map data 116 can be highquality and/or highly detailed. The terrain map(s) 117 can define one ormore ground surfaces, which can include paved roads, unpaved roads,land, and other things that define a ground surface.

In one or more arrangements, the map data 116 can include one or morestatic obstacle maps 118. The static obstacle map(s) 118 can includeinformation about one or more static obstacles located within one ormore geographic areas. A “static obstacle” is a physical object whoseposition does not change or substantially change over a period of timeand/or whose size does not change or substantially change over a periodof time. Examples of static obstacles include trees, buildings, curbs,fences, railings, medians, utility poles, statues, monuments, signs,benches, furniture, mailboxes, large rocks, hills. The static obstaclescan be objects that extend above ground level. The one or more staticobstacles included in the static obstacle map(s) 118 can have locationdata, size data, dimension data, material data, and/or other dataassociated with it. The static obstacle map(s) 118 can includemeasurements, dimensions, distances, and/or information for one or morestatic obstacles. The static obstacle map(s) 118 can be high qualityand/or highly detailed. The static obstacle map(s) 118 can be updated toreflect changes within a mapped area.

The one or more data stores 115 can include sensor data 119. In thiscontext, “sensor data” means any information about the sensors that thevehicle 100 is equipped with, including the capabilities and otherinformation about such sensors. As will be explained below, the vehicle100 can include the sensor system 120. The sensor data 119 can relate toone or more sensors of the sensor system 120. As an example, in one ormore arrangements, the sensor data 119 can include information on one ormore LIDAR sensors 124 of the sensor system 120.

In some instances, at least a portion of the map data 116 and/or thesensor data 119 can be located in one or more data stores 115 locatedonboard the vehicle 100. Alternatively, or in addition, at least aportion of the map data 116 and/or the sensor data 119 can be located inone or more data stores 115 that are located remotely from the vehicle100.

As noted above, the vehicle 100 can include the sensor system 120. Thesensor system 120 can include one or more sensors. “Sensor” means anydevice, component and/or system that can detect, and/or sense something.The one or more sensors can be configured to detect, and/or sense inreal-time. As used herein, the term “real-time” means a level ofprocessing responsiveness that a user or system senses as sufficientlyimmediate for a particular process or determination to be made, or thatenables the processor to keep up with some external process.

In arrangements in which the sensor system 120 includes a plurality ofsensors, the sensors can work independently from each other.Alternatively, two or more of the sensors can work in combination witheach other. In such a case, the two or more sensors can form a sensornetwork. The sensor system 120 and/or the one or more sensors can beoperatively connected to the processor(s) 110, the data store(s) 115,and/or another element of the vehicle 100 (including any of the elementsshown in FIG. 1 ). The sensor system 120 can acquire data of at least aportion of the external environment of the vehicle 100 (e.g., nearbyvehicles).

The sensor system 120 can include any suitable type of sensor. Variousexamples of different types of sensors will be described herein.However, it will be understood that the embodiments are not limited tothe particular sensors described. The sensor system 120 can include oneor more vehicle sensors 121. The vehicle sensor(s) 121 can detect,determine, and/or sense information about the vehicle 100 itself. In oneor more arrangements, the vehicle sensor(s) 121 can be configured todetect, and/or sense position and orientation changes of the vehicle100, such as, for example, based on inertial acceleration. In one ormore arrangements, the vehicle sensor(s) 121 can include one or moreaccelerometers, one or more gyroscopes, an inertial measurement unit(IMU), a dead-reckoning system, a global navigation satellite system(GNSS), a global positioning system (GPS), a navigation system 147,and/or other suitable sensors. The vehicle sensor(s) 121 can beconfigured to detect, and/or sense one or more characteristics of thevehicle 100. In one or more arrangements, the vehicle sensor(s) 121 caninclude a speedometer to determine a current speed of the vehicle 100.

Alternatively, or in addition, the sensor system 120 can include one ormore environment sensors 122 configured to acquire, and/or sense drivingenvironment data. “Driving environment data” includes data orinformation about the external environment in which an autonomousvehicle is located or one or more portions thereof. For example, the oneor more environment sensors 122 can be configured to detect, quantifyand/or sense obstacles in at least a portion of the external environmentof the vehicle 100 and/or information/data about such obstacles. Suchobstacles may be stationary objects and/or dynamic objects. The one ormore environment sensors 122 can be configured to detect, measure,quantify and/or sense other things in the external environment of thevehicle 100, such as, for example, lane markers, signs, traffic lights,traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100,off-road objects, etc.

Various examples of sensors of the sensor system 120 will be describedherein. The example sensors may be part of the one or more environmentsensors 122 and/or the one or more vehicle sensors 121. However, it willbe understood that the embodiments are not limited to the particularsensors described.

As an example, in one or more arrangements, the sensor system 120 caninclude one or more radar sensors 123, one or more LIDAR sensors 124,one or more sonar sensors 125, and/or one or more cameras 126. In one ormore arrangements, the one or more cameras 126 can be high dynamic range(HDR) cameras or infrared (IR) cameras.

The vehicle 100 can include an input system 130. An “input system”includes any device, component, system, element, or arrangement orgroups thereof that enable information/data to be entered into amachine. The input system 130 can receive an input from a vehiclepassenger (e.g., a driver or a passenger). The vehicle 100 can includean output system 135. An “output system” includes any device, component,or arrangement or groups thereof that enable information/data to bepresented to a vehicle passenger (e.g., a person, a vehicle passenger,etc.).

The vehicle 100 can include one or more vehicle systems 140. Variousexamples of the one or more vehicle systems 140 are shown in FIG. 1 .However, the vehicle 100 can include more, fewer, or different vehiclesystems. It should be appreciated that although particular vehiclesystems are separately defined, each or any of the systems or portionsthereof may be otherwise combined or segregated via hardware and/orsoftware within the vehicle 100. The vehicle 100 can include apropulsion system 141, a braking system 142, a steering system 143,throttle system 144, a transmission system 145, a signaling system 146,and/or a navigation system 147. Each of these systems can include one ormore devices, components, and/or a combination thereof, now known orlater developed.

The navigation system 147 can include one or more devices, applications,and/or combinations thereof, now known or later developed, configured todetermine the geographic location of the vehicle 100 and/or to determinea travel route for the vehicle 100. The navigation system 147 caninclude one or more mapping applications to determine a travel route forthe vehicle 100. The navigation system 147 can include a globalpositioning system, a local positioning system, or a geolocation system.

The processor(s) 110, the depth system 170, and/or the autonomousdriving module(s) 160 can be operatively connected to communicate withthe various vehicle systems 140 and/or individual components thereof.For example, returning to FIG. 1 , the processor(s) 110 and/or theautonomous driving module(s) 160 can be in communication to send and/orreceive information from the various vehicle systems 140 to control themovement, speed, maneuvering, heading, direction, etc. of the vehicle100. The processor(s) 110, the depth system 170, and/or the autonomousdriving module(s) 160 may control some or all of these vehicle systems140 and, thus, may be partially or fully autonomous.

The processor(s) 110, the depth system 170, and/or the autonomousdriving module(s) 160 can be operatively connected to communicate withthe various vehicle systems 140 and/or individual components thereof.For example, returning to FIG. 1 , the processor(s) 110, the depthsystem 170, and/or the autonomous driving module(s) 160 can be incommunication to send and/or receive information from the variousvehicle systems 140 to control the movement, speed, maneuvering,heading, direction, etc. of the vehicle 100. The processor(s) 110, thedepth system 170, and/or the autonomous driving module(s) 160 maycontrol some or all of these vehicle systems 140.

The processor(s) 110, the depth system 170, and/or the autonomousdriving module(s) 160 may be operable to control the navigation and/ormaneuvering of the vehicle 100 by controlling one or more of the vehiclesystems 140 and/or components thereof. For instance, when operating inan autonomous mode, the processor(s) 110, the depth system 170, and/orthe autonomous driving module(s) 160 can control the direction and/orspeed of the vehicle 100. The processor(s) 110, the depth system 170,and/or the autonomous driving module(s) 160 can cause the vehicle 100 toaccelerate (e.g., by increasing the supply of fuel provided to theengine), decelerate (e.g., by decreasing the supply of fuel to theengine and/or by applying brakes) and/or change direction (e.g., byturning the front two wheels). As used herein, “cause” or “causing”means to make, force, compel, direct, command, instruct, and/or enablean event or action to occur or at least be in a state where such eventor action may occur, either in a direct or indirect manner.

The vehicle 100 can include one or more actuators 150. The actuators 150can be any element or combination of elements operable to modify, adjustand/or alter one or more of the vehicle systems 140 or componentsthereof to responsive to receiving signals or other inputs from theprocessor(s) 110 and/or the autonomous driving module(s) 160. Anysuitable actuator can be used. For instance, the one or more actuators150 can include motors, pneumatic actuators, hydraulic pistons, relays,solenoids, and/or piezoelectric actuators, just to name a fewpossibilities.

The vehicle 100 can include one or more modules, at least some of whichare described herein. The modules can be implemented ascomputer-readable program code that, when executed by a processor 110,implement one or more of the various processes described herein. One ormore of the modules can be a component of the processor(s) 110, or oneor more of the modules can be executed on and/or distributed among otherprocessing systems to which the processor(s) 110 is operativelyconnected. The modules can include instructions (e.g., program logic)executable by one or more processor(s) 110. Alternatively, or inaddition, one or more data store 115 may contain such instructions.

In one or more arrangements, one or more of the modules described hereincan include artificial or computational intelligence elements, e.g.,neural network, fuzzy logic or other machine learning algorithms.Further, in one or more arrangements, one or more of the modules can bedistributed among a plurality of the modules described herein. In one ormore arrangements, two or more of the modules described herein can becombined into a single module.

The vehicle 100 can include one or more autonomous driving modules 160.The autonomous driving module(s) 160 can be configured to receive datafrom the sensor system 120 and/or any other type of system capable ofcapturing information relating to the vehicle 100 and/or the externalenvironment of the vehicle 100. In one or more arrangements, theautonomous driving module(s) 160 can use such data to generate one ormore driving scene models. The autonomous driving module(s) 160 candetermine position and velocity of the vehicle 100. The autonomousdriving module(s) 160 can determine the location of obstacles,obstacles, or other environmental features including traffic signs,trees, shrubs, neighboring vehicles, pedestrians, etc.

The autonomous driving module(s) 160 can be configured to receive,and/or determine location information for obstacles within the externalenvironment of the vehicle 100 for use by the processor(s) 110, and/orone or more of the modules described herein to estimate position andorientation of the vehicle 100, vehicle position in global coordinatesbased on signals from a plurality of satellites, or any other dataand/or signals that could be used to determine the current state of thevehicle 100 or determine the position of the vehicle 100 with respect toits environment for use in either creating a map or determining theposition of the vehicle 100 in respect to map data.

The autonomous driving module(s) 160 either independently or incombination with the depth system 170 can be configured to determinetravel path(s), current autonomous driving maneuvers for the vehicle100, future autonomous driving maneuvers and/or modifications to currentautonomous driving maneuvers based on data acquired by the sensor system120, driving scene models, and/or data from any other suitable source.“Driving maneuver” means one or more actions that affect the movement ofa vehicle. Examples of driving maneuvers include: accelerating,decelerating, braking, turning, moving in a lateral direction of thevehicle 100, changing travel lanes, merging into a travel lane, and/orreversing, just to name a few possibilities. The autonomous drivingmodule(s) 160 can be configured to implement determined drivingmaneuvers. The autonomous driving module(s) 160 can cause, directly orindirectly, such autonomous driving maneuvers to be implemented. As usedherein, “cause” or “causing” means to make, command, instruct, and/orenable an event or action to occur or at least be in a state where suchevent or action may occur, either in a direct or indirect manner. Theautonomous driving module(s) 160 can be configured to execute variousvehicle functions and/or to transmit data to, receive data from,interact with, and/or control the vehicle 100 or one or more systemsthereof (e.g., one or more of vehicle systems 140).

Detailed embodiments are disclosed herein. However, it is to beunderstood that the disclosed embodiments are intended only as examples.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative basis for teaching one skilled in the artto variously employ the aspects herein in virtually any appropriatelydetailed structure. Further, the terms and phrases used herein are notintended to be limiting but rather to provide an understandabledescription of possible implementations. Various embodiments are shownin FIGS. 1-6 , but the embodiments are not limited to the illustratedstructure or application.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flowcharts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved.

The systems, components and/or processes described above can be realizedin hardware or a combination of hardware and software and can berealized in a centralized fashion in one processing system or in adistributed fashion where different elements are spread across severalinterconnected processing systems. Any kind of processing system oranother apparatus adapted for carrying out the methods described hereinis suited. A typical combination of hardware and software can be aprocessing system with computer-usable program code that, when beingloaded and executed, controls the processing system such that it carriesout the methods described herein. The systems, components and/orprocesses also can be embedded in a computer-readable storage, such as acomputer program product or other data programs storage device, readableby a machine, tangibly embodying a program of instructions executable bythe machine to perform methods and processes described herein. Theseelements also can be embedded in an application product which comprisesall the features enabling the implementation of the methods describedherein and, which when loaded in a processing system, is able to carryout these methods.

Furthermore, arrangements described herein may take the form of acomputer program product embodied in one or more computer-readable mediahaving computer-readable program code embodied, e.g., stored, thereon.Any combination of one or more computer-readable media may be utilized.The computer-readable medium may be a computer-readable signal medium ora computer-readable storage medium. The phrase “computer-readablestorage medium” means a non-transitory storage medium. Acomputer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: a portablecomputer diskette, a hard disk drive (HDD), a solid-state drive (SSD), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), adigital versatile disc (DVD), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer-readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

Generally, module, as used herein, includes routines, programs, objects,components, data structures, and so on that perform particular tasks orimplement particular data types. In further aspects, a memory generallystores the noted modules. The memory associated with a module may be abuffer or cache embedded within a processor, a RAM, a ROM, a flashmemory, or another suitable electronic storage medium. In still furtheraspects, a module as envisioned by the present disclosure is implementedas an application-specific integrated circuit (ASIC), a hardwarecomponent of a system on a chip (SoC), as a programmable logic array(PLA), or as another suitable hardware component that is embedded with adefined configuration set (e.g., instructions) for performing thedisclosed functions.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber, cable, RF, etc., or any suitable combination ofthe foregoing. Computer program code for carrying out operations foraspects of the present arrangements may be written in any combination ofone or more programming languages, including an object-orientedprogramming language such as Java™ Smalltalk, C++ or the like andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a standalone software package, partly on the user'scomputer and partly on a remote computer, or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

The terms “a” and “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more. The terms “including” and/or “having,” as used herein,are defined as comprising (i.e., open language). The phrase “at leastone of . . . and . . . ” as used herein refers to and encompasses anyand all possible combinations of one or more of the associated listeditems. As an example, the phrase “at least one of A, B, and C” includesA only, B only, C only, or any combination thereof (e.g., AB, AC, BC orABC).

Aspects herein can be embodied in other forms without departing from thespirit or essential attributes thereof. Accordingly, reference should bemade to the following claims, rather than to the foregoingspecification, as indicating the scope hereof.

What is claimed is:
 1. A depth system, comprising: one or moreprocessors; a memory communicably coupled to the one or more processorsand storing: a network module including instructions that, when executedby the one or more processors, cause the one or more processors to:generate depth features from sensor data according to whether the sensordata includes sparse depth data, including encoding the sparse depthdata from a range sensor to form the depth features, selectively injectthe depth features into a depth model via skip connections of the depthmodel when the depth features are available according to a presence ofthe sparse depth data in the sensor data, generate a depth map from atleast a monocular image using the depth model that is guided by thedepth features representing explicit depths from the range sensor wheninjected, and provide the depth map as depth estimates of objectsrepresented in the monocular image.
 2. The depth system of claim 1,wherein the network module includes instructions to generate the depthfeatures including instructions to use a sparse auxiliary network thatis a convolutional encoder to generate the depth features from thesparse depth data during inference, and wherein the sparse depth data ispart of the sensor data that is acquired from the range sensor.
 3. Thedepth system of claim 1, wherein the network module includesinstructions to selectively inject the depth features includinginstructions to, in response to determining that the sensor dataincludes the sparse depth data, inject the depth features into the depthmodel by concatenating the depth features with image features from anencoder of the depth model and provide concatenated features into adecoder of the depth model.
 4. The depth system of claim 3, wherein thenetwork module includes instructions to inject the depth featuresincluding instructions to apply learned weights to the depth featuresand the image features prior to concatenating via the skip connectionsof the depth model.
 5. The depth system of claim 1, wherein the networkmodule includes instructions to generate the depth map includinginstructions to apply the depth model to the monocular image by using anencoder of the depth model to encode image features and to use a decoderof the depth model to decode the depth features into the depth map, andwherein the network module includes instructions to decode the imagefeatures at separate spatial resolutions as provided by the skipconnections between the encoder and the decoder in combination with anoutput of a previous layer of the decoder.
 6. The depth system of claim1, wherein the network module includes instructions to acquire thesensor data including at least the monocular image from at least onesensor of a device, wherein the network module includes instructions togenerate the depth features including instructions to determine whetherthe sensor data includes sparse depth data in addition to the monocularimage and activating a sparse auxiliary network to generate the depthfeatures from the sparse depth data when the sparse depth data ispresent.
 7. The depth system of claim 1, wherein providing the depth mapincludes controlling a device to navigate through a surroundingenvironment according to the depth map that identifies distances toobjects in the surrounding environment.
 8. The depth system of claim 1,wherein the depth system is integrated within a device for autonomouslycontrolling a vehicle.
 9. A non-transitory computer-readable mediumincluding instructions that, when executed by one or more processorscause the one or more processors to: generate depth features from sensordata according to whether the sensor data includes sparse depth data,including encoding the sparse depth data from a range sensor to form thedepth features, selectively inject the depth features into a depth modelvia skip connections of the depth model when the depth features areavailable according to a presence of the sparse depth data in the sensordata, generate a depth map from at least a monocular image using thedepth model that is guided by the depth features representing explicitdepths from the range sensor when injected, and provide the depth map asdepth estimates of objects represented in the monocular image.
 10. Thenon-transitory computer-readable medium of claim 9, wherein theinstructions to generate the depth features include instructions to usea sparse auxiliary network that is a convolutional encoder to generatethe depth features from the sparse depth data, and wherein the sparsedepth data is part of the sensor data that is acquired from a rangesensor.
 11. The non-transitory computer-readable medium of claim 9,wherein the instructions to selectively inject the depth featuresinclude instructions to, in response to determining that the sensor dataincludes the sparse depth data, inject the depth features into the depthmodel by concatenating the depth features with image features from anencoder of the depth model and provide concatenated features into adecoder of the depth model.
 12. The non-transitory computer-readablemedium of claim 11, wherein the instructions to inject the depthfeatures include instructions to apply learned weights to the depthfeatures and the image features prior to concatenating via the skipconnections of the depth model.
 13. The non-transitory computer-readablemedium of claim 9, wherein instructions to generate the depth mapinclude instructions to apply the depth model to the monocular image byusing an encoder of the depth model to encode image features and to usea decoder of the depth model to decode the depth features into the depthmap, and wherein the instructions to decode the image features atseparate spatial resolutions as provided by the skip connections betweenthe encoder and the decoder in combination with an output of a previouslayer of the decoder.
 14. A method, comprising: generating depthfeatures from sensor data according to whether the sensor data includessparse depth data, including encoding the sparse depth data from a rangesensor to form the depth features; selectively injecting the depthfeatures into a depth model via skip connections of the depth model whenthe depth features are available according to a presence of the sparsedepth data in the sensor data; generating a depth map from at least amonocular image using the depth model that is guided by the depthfeatures representing explicit depths from the range sensor wheninjected; and providing the depth map as depth estimates of objectsrepresented in the monocular image.
 15. The method of claim 14, whereingenerating the depth features includes using a sparse auxiliary networkthat is a convolutional encoder to generate the depth features from thesparse depth data during inference, and wherein the sparse depth data ispart of the sensor data that is acquired from a range sensor.
 16. Themethod of claim 14, wherein selectively injecting the depth featuresincludes, in response to determining that the sensor data includes thesparse depth data, injecting the depth features into the depth model byconcatenating the depth features with image features from an encoder ofthe depth model and providing concatenated features into a decoder ofthe depth model.
 17. The method of claim 16, wherein injecting the depthfeatures includes applying learned weights to the depth features and theimage features prior to concatenating via the skip connections of thedepth model.
 18. The method of claim 14, wherein generating the depthmap includes applying the depth model to the monocular image by using anencoder of the depth model to encode image features and to use a decoderof the depth model to decode the depth features into the depth map, andwherein using the decoder includes decoding the image features atseparate spatial resolutions as provided by the skip connections betweenthe encoder and the decoder in combination with an output of a previouslayer of the decoder.
 19. The method of claim 14, further comprising:acquiring the sensor data including at least the monocular image from atleast one sensor of a device, wherein generating the depth featuresincludes determining whether the sensor data includes sparse depth datain addition to the monocular image and activating a sparse auxiliarynetwork to generate the depth features from the sparse depth data whenthe sparse depth data is present.
 20. The method of claim 14, whereinproviding the depth map includes controlling a device to navigatethrough a surrounding environment according to the depth map thatidentifies distances to objects in the surrounding environment.